78 results about "Risk impact" patented technology

The invention provides an interaction method and system for thromboembolism risk assessment, and can be applied to the technical field of artificial intelligence. The method comprises the steps that a first server side responds to an inquiry request sent by a first terminal, and inquiry diagnosis data and historical clinical data are sent to a second server side; the second server obtains an initial risk assessment score according to the inquiry diagnosis data and the historical clinical data; under the condition that the initial risk assessment score is greater than a preset risk threshold value, the second server side sends inquiry diagnosis data and historical clinical data to a second terminal; the second terminal determines risk influence factors according to the inquiry diagnosis data and the historical clinical data; the second server obtains the detection result, and performs hospital thromboembolism risk assessment on the detection data by using a Bayesian network to obtain a target risk assessment result; and the second server determines a rehabilitation guidance strategy according to the target risk assessment result and the discharge diagnosis result, and sends the rehabilitation guidance strategy to the first terminal.

The invention provides a level protection-oriented network security configuration checking method, which comprises the following steps of: constructing a knowledge graph associated with a security control item and a technical implementation mode by relying on a standard document and configuration data, and implementing a control item logic dependency and semantic weight reconstruction model; security configuration of assets is automatically collected and semantically mapped to map nodes, configuration deviation is recognized, a risk influence path template is matched in combination with asset importance and service context, semantic propagation path influence intensity is dynamically calculated, and an accumulated risk influence value of configuration deviation is generated in an aggregation mode; according to the method, risk level judgment is carried out by adopting a piecewise nonlinear function, a structured risk assessment record is output, risk identification and reinforcement suggestion closed loop are realized, and the hierarchical management and control and automatic compliance response capability of security configuration deviation in a network environment is improved.

The invention discloses a project combination adjusting method, and particularly relates to a project combination adjusting method and device based on dynamic risks and electronic equipment. The method comprises the following steps: acquiring support degree information corresponding to an existing project combination at a moment t-1, and acquiring risk influence information at a moment t; updating the support degree information based on the risk influence information to obtain updated support degree information at the moment t; judging whether the existing project combination meets a preset constraint condition or not based on the updated support degree information at the moment t; under the condition that the existing project combination does not meet the preset constraint condition, the adjustment cost corresponding to each project adjustment scheme is calculated, and the project adjustment scheme comprises an adjustment strategy executed by each existing project; and adjusting the existing project combination based on the project adjustment scheme with the lowest adjustment cost to obtain an updated project combination at the moment t. According to the method, the project combination can be dynamically adjusted according to the risk impact, and efficient allocation of resources is realized.

The invention discloses a wharf risk assessment and management method based on artificial intelligence, particularly relates to the field of wharf risk assessment and management, and is used for solving the problems that an existing wharf is difficult to quantify and delay influence on a supply chain network level, and prospective intervention cannot be performed on supply chain risks. According to the method, a comprehensive data platform of wharf operation and supply chain cargo linkage is constructed, and the bottleneck time point of a future delivery window is analyzed and predicted based on the ship arrival density, the operation bearing capacity and the stockpiling structure; searching a historical supply chain interruption event and extracting corresponding cargo and node information; tracking a conduction path of an interruption event in the supply chain network, quantifying the influence of a bottleneck on a downstream node, and establishing a supply chain risk influence model; generating a supply chain risk influence map in combination with future bottleneck prediction; and finally, a composite risk portrait is constructed, and targeted risk intervention is carried out on the wharf and the supply chain node. And intelligent prediction and management of wharf and supply chain full-chain risks are realized.

The invention provides a small hydropower station safety and ecological multi-source risk dynamic assessment method and system, and belongs to the technical field of hydropower station management. Constructing a multi-dimensional risk feature tensor, including preprocessing multi-source data to obtain standardized data, extracting features from the standardized data, organizing the features according to risk types, time, space and feature four-dimensional organizations to form a four-order tensor, and performing decomposition to extract a core risk mode; constructing a conditional risk probability field, including conditional probability density estimation, risk probability field dynamic evolution model establishment and multi-source information entropy weight fusion; constructing a risk degree assessment model, including risk influence degree matrix construction, matrix spectrum analysis and risk measurement generation; according to the method, the defects of single dimension, static evaluation, high subjectivity and the like in the prior art are overcome, and accurate quantification and dynamic evaluation of the safety and ecological risk of the small hydropower station are realized.

The invention discloses a field operation video monitoring risk prevention and control method, system and device and a medium, and the method comprises the steps: analyzing the reference operation risks of the whole network over the years, and constructing a reference operation risk library according to the analysis result; establishing a first risk model through the reference operation risk library, and identifying risk influence factors; constructing a second risk model through the risk influence factors, determining weight values of the risk influence factors, outputting risk values, and performing risk grade division according to the risk values; deploying video monitoring equipment to identify operating personnel, synchronously monitoring an operation site, and analyzing and judging an abnormal condition through a pixel difference value of front and back frame images; and when the abnormal situation is judged, determining an early warning level in combination with the risk level, and giving an alarm. According to the method, historical risk data analysis and an intelligent video monitoring technology are deeply fused, so that risk assessment is fundamentally converted from static experience judgment to dynamic quantitative prediction.

The invention discloses a cross-organization collaborative project risk and progress coupling early warning method and system, and the method comprises the steps: obtaining risk data and progress data of a project and external environment data corresponding to the risk data, the risk data comprising a risk occurrence probability, and the progress data comprising a planned progress completion rate and an actual progress completion rate; determining the product of the risk occurrence probability and the risk influence degree as a risk dominant coefficient; determining an absolute value of a difference value between the actual progress completion rate and the planned progress completion rate as a progress dominant coefficient; when it is determined that the risk dominant coefficient is greater than or equal to the progress dominant coefficient, inputting the risk data and the external environment data into a preset risk-progress model to obtain first early warning information; and when it is determined that the progress dominant coefficient is greater than the risk dominant coefficient, inputting the progress data to a preset progress-risk model to obtain second early warning information. The project risk and progress early warning method is used for improving the accuracy of project risk and progress early warning.

The present invention relates to the technical field of international trade risk management, and particularly relates to an intelligent international trade risk prevention and control method and system based on multi-modal data analysis. The method comprises: collecting transaction records, logistics information, policies, laws and regulations, and dynamic market data; extracting abnormal behavior features and evaluating a transaction risk grade; in view of logistics and policy data, evaluating the degree of influence of a cross-border transportation risk, so as to obtain a comprehensive risk index; and by means of the dynamic market data, estimating an overall risk level for early warning. The method can improve the accuracy and real-time response capability of international trade risk prediction, thereby meeting intelligent risk management requirements in complex environments.

The invention discloses a risk assessment method and device, electronic equipment, a storage medium and a program product, and aims to solve the problems of relatively high labor cost and relatively low risk assessment efficiency when an existing risk assessment mode is adopted. The method comprises the steps of obtaining historical claim settlement data of a position area where a target property insurance object is located; determining a conduction risk value of the historical claim settlement event to the target property insurance object according to the historical claim settlement data, and determining an aggregation risk value of the claim settlement disaster cause of the historical claim settlement event to the target property insurance object; wherein the conduction risk value is used for representing the risk influence degree of the historical claim settlement event on the target property insurance object; the aggregation risk is used for representing a nonlinear risk amplification effect generated by the interaction relationship among the multiple claim settlement disaster causes on the target property insurance object; determining an environment risk value of a position area where the target property insurance object is located; and evaluating risk information of the target property insurance object according to the conduction risk value, the aggregation risk value and the environment risk value.

The application discloses a kind of tunnel abnormal event-oriented risk assessment method and system, it is related to tunnel traffic safety management technical field.The method includes: collecting tunnel operating state data;Generate abnormal event basic data;According to the available sensing distance and the required braking distance of abnormal event occurrence section and its associated section, generate structure sensitive coefficient, and section correction is carried out to abnormal event basic risk value;With abnormal event occurrence section as risk source section, in combination with structure sensitive correction data, propagation direction, distance attenuation and lane disturbance state calculate the propagation risk potential field value of each tunnel section;Determine abnormal event risk assessment result.The application can convert tunnel abnormal event into section-level risk assessment result from single-point alarm, improve the accuracy of tunnel abnormal event risk influence range identification and emergency disposal decision.

The application discloses a wharf risk assessment and management method based on artificial intelligence, and particularly relates to the field of wharf risk assessment and management, and is used for solving the problems that the existing wharf is difficult to quantize the influence of delay on the supply chain network level and cannot intervene in the supply chain risk in advance. The application builds a comprehensive data platform of wharf operation and supply chain cargo linkage, analyzes and predicts the future delivery window bottleneck time point based on the ship arrival density, operation carrying capacity and stacking structure, retrieves the historical supply chain interruption events and extracts the corresponding cargo and node information, tracks the conduction path of the interruption event in the supply chain network, quantizes the influence of the bottleneck on the downstream node and establishes a supply chain risk influence model, generates a supply chain risk influence graph in combination with the future bottleneck prediction, and finally builds a composite risk portrait and implements targeted risk intervention on the wharf and the supply chain node. The application realizes intelligent prediction and management of the wharf and the supply chain whole-chain risk.

This invention discloses a construction emergency early warning method and system. The method includes the following steps: collecting the three-dimensional coordinates of construction workers, using a sliding time window combined with indicators such as wavelet packet energy entropy, and generating a multi-scale motion disorder index through principal component analysis; identifying continuously unstable personnel based on the disorder index, segmenting their trajectories, and constructing a weighted graph by combining Hidden Markov Model and Building Information Model environmental parameters to calculate cognitive mismatch; mapping the cognitive mismatch to a spatial grid, calculating the local Moran index, and fusing density gradient and mechanical operation sequence resonance results to construct a propagation weight matrix; guiding the construction of a heterogeneous graph based on the weight matrix, calculating the risk impact propagation potential energy, and coupling historical disorder sequence analysis and relative entropy to obtain a group-level instability prediction value; for individuals whose prediction values ​​exceed the limit, outputting a comprehensive crisis level through motion trend prediction, spatial intersection, and shortest path algorithms. This invention improves the safety of construction operations.

The invention relates to the technical field of intelligent power distribution networks, and discloses a power distribution network information physical coupling risk propagation deduction method, system and device based on a hidden Markov model, and a medium. The method comprises the following steps: establishing a power distribution information physical system, an information physical coupling risk event model and a power distribution network information physical coupling risk event propagation model to effectively deduce the propagation of the power distribution network information physical coupling risk. According to the method, complex cyber-physical coupling risk events are disassembled into independent risk event sets, the characteristics of each risk event can be deeply analyzed, risk influences are accurately evaluated according to multivariable information such as the starting side, the hazard loss type, the occurrence probability and the influence range, the propagation mechanism of risks in different levels is clearly described, and the risk assessment efficiency is improved. And a worker can quickly position a risk source and take effective measures to block risk propagation.

The application relates to the technical field of supply chain risk assessment, in particular to a supply chain risk assessment system based on multi-source data fusion, which comprises a data acquisition module, a multi-source data acquisition end is configured, a binding relationship between the acquisition end and each node in a database is established, wherein the database is used for storing multi-source data related to supply chain risk assessment; a data fusion module is used for constructing a knowledge graph based on the multi-source data, wherein the nodes of the knowledge graph correspond to risk influence factors, and the edges represent the correlation between the factors; a model construction module is used for constructing a risk assessment model based on the knowledge graph; a risk assessment module is used for performing risk assessment on the supply chain based on the risk assessment model to obtain a risk assessment result; through the fusion of the multi-source data, the change of the multi-source data is monitored in real time, the dynamic change in the supply chain is responded in real time, the evaluation strategy is adjusted in a timely manner, and the accuracy and timeliness of the evaluation result are improved.

The embodiment of the invention provides a power transmission line icing falling jump risk influence factor analysis method and device, and the method comprises the steps: determining a risk index value under each working condition of a power transmission line and a line deicing jump influence factor, and enabling the risk index value to be related to the maximum jump height of a lead of the power transmission line; constructing a first matrix based on the working conditions, the influence factors and the risk index values; performing standardization processing on the first matrix by adopting a range standardization algorithm to obtain a second matrix; determining a standard deviation of each influence factor based on the second matrix; and determining the weight of each influence factor based on the standard deviation of each influence factor, wherein the weight represents the influence degree of the influence factor on the icing shedding jump risk of the power transmission line.

The invention relates to a karst collapse multi-modal early warning method and system based on a space-time fusion attention mechanism, and aims to realize accurate prediction and early warning of a stratum collapse risk through multi-modal data fusion and the space-time fusion attention mechanism. According to the method, firstly, multi-modal data such as horizontal strain, vibration, stratum settlement displacement and underground water flow velocity are collected through a horizontal measuring pipe and a vertical drill hole, and space and time characteristic tensors are constructed; then, a space-time fusion attention mechanism is adopted to distribute weights for different space positions and time points so as to highlight areas and moments which have great influence on the stratum collapse risk; the model extracts space and time features through CNN and LSTM, comprehensively analyzes the risk contribution of environment, stratum structure and physical perception data through a risk operator, and finally generates a risk early warning signal. The method can monitor the change of the stratum in real time and generate early warning, and has the advantages of multi-modal data fusion, space-time weight dynamic adjustment, accurate risk assessment and the like.

The invention discloses a flood early-warning decision analysis method based on deep learning, and relates to the technical field of flood early-warning decision, and the method comprises the steps: collecting and aligning rainfall, water level, flow, engineering working condition and exposure data, generating probabilistic risk degree and impact evaluation through combining with a flood or waterlogging prediction module, and carrying out the early-warning decision analysis. Constructing risk-influence objects indexed according to space units and time slices; response telemetering is formed based on early warning sending, reading and handling receipts, and a credibility state and an attention budget state are recursively derived according to crowds and channels; under security constraints and attention budget constraints, candidate early warning strategies including early warning levels, publishing objects, publishing channels and action suggestion packets are graded and screened, graded pushing and disposal distribution are implemented according to selected early warning publishing strategies, relevant parameters are updated through auditing records and post-disaster true value recharge, and the early warning publishing strategies are distributed according to the updated parameters. Therefore, the conversion efficiency of the early warning action is improved while the invalid alarm and the disposal consumption are reduced.

The invention discloses a construction project quality safety risk big data analysis and intelligent early warning system, and particularly relates to the technical field of construction project risk management and big data analysis, and the method comprises the steps: collecting risk impact factor multi-source data covering a construction project; constructing an engineering knowledge graph containing a space-time association rule, and calculating a risk level; judging a risk level and predicting a risk space-time diffusion path, and executing differential grading early warning; an optimal disposal scheme is screened through simulation rehearsal of a digital twinborn model, and full-process closed-loop disposal management is constructed; and updating the knowledge graph. According to the invention, a multi-source heterogeneous data fusion preprocessing module, a knowledge graph embedding and deep learning hybrid dynamic risk assessment module, a time-space association and hierarchical intelligent early warning module, a digital twin closed-loop disposal tracking module and a knowledge transfer learning graph management module are adopted; the problems of insufficient data fusion capability, static and one-sided risk assessment, disjunction of early warning and disposal and the like are solved.

The invention discloses a historical building dynamic risk assessment method and system, and relates to the technical field of risk assessment, and the method comprises the steps: carrying out defect space-time change trajectory marking according to defect space distribution density data, obtaining defect space-time change trajectory data, carrying out defect evolution trend estimation based on the defect space-time change trajectory data, and carrying out risk assessment. Defect evolution trend data is obtained, historical building risk grade reference evaluation is carried out according to the defect evolution trend data, and risk grade reference data is obtained; performing environmental impact factor identification on the defect evolution trend data according to historical building environmental monitoring data to obtain an environmental association impact factor, and performing risk impact dynamic correction on the risk level reference data to obtain risk impact correction data; and performing dynamic risk assessment model construction on the risk influence correction data to obtain a historical building dynamic risk assessment model. The historical building dynamic risk assessment method has the effect of improving historical building dynamic risk assessment efficiency.

The present invention is a computer system for creating fractal spider graphs which display data in a nested structure. The present invention includes a data store, a data analytics engines, and a computer platform for data visualization. The present invention analyzes both qualitative and quantitative data to determine a pattern of behavior based on the data. The data is then broken down into key performance indicator (KPI) units using NLP and machine learning. These KPI units are then used as the atomic unit for constructing the fractal spider graphs. The fractal spider graphs of the present invention are hierarchical, nested spider graphs which simultaneously display information and analysis from multiple levels of an organizational hierarchy. Further, the present invention is operable to generate inferences (i.e., decision outputs) based on the data, wherein the inferences are displayed as prioritized action lists, weighted confidence scores, risk-impact indices, and / or narrative recommendations.

The invention belongs to the technical field of power grid disaster prevention and reduction, and particularly relates to a risk assessment method and device for deicing jump of a power transmission line, electronic equipment and a computer readable storage medium. The method comprises the following steps: determining risk influence factors influencing the deicing jump of the power transmission line, and constructing an original parameter data set containing a plurality of deicing working conditions; establishing a conductor deicing model under a plurality of deicing working conditions and carrying out analogue simulation to obtain a risk consequence index; constructing a standardized decision matrix, respectively calculating the comparison intensity and conflict of each risk influence factor, and calculating the comprehensive information amount and objective weight of each risk influence factor to obtain an objective weight vector; and identifying the key dominant factors based on the objective weight vector and carrying out working condition optimization in combination with the risk consequence indexes under each deicing working condition to obtain the optimal deicing working condition with the lowest comprehensive risk. According to the method, the CRITIC method is applied to power transmission line deicing jump risk assessment, and the deicing working condition with small risk can be selected.

The invention discloses a bidding and tendering risk value calculation method and system based on deep learning, and relates to the technical field of data mining and risk assessment crossing. A weighted analysis and time sequence change identification method is adopted to carry out risk influence degree importance evaluation and dynamic change trend identification of the space-time dimension on the bidding and tendering risk time sequence data set to form a time sequence risk change result, and the time sequence risk change result is fused with the multi-dimensional risk feature vector to construct a fusion risk feature matrix; performing risk relevancy calculation on the fusion risk feature matrix by using an adaptive optimization algorithm to generate a comprehensive risk index set; and according to the comprehensive risk index set, carrying out risk level mapping through principal component risk compression and clustering mode identification, and generating a bidding risk assessment report. According to the invention, by generating the bidding and tendering risk assessment report, dimension reduction optimization of complex risk features and automatic identification of a high-risk mode are realized, and the accuracy and interpretability of risk assessment are enhanced.

The invention discloses a distributed photovoltaic cluster operation risk assessment method based on multi-dimensional observability, and relates to the technical field of risk assessment, and the method comprises the following steps: obtaining an environment interval score according to the obtained environment data and baseline height of a photovoltaic array, and obtaining an environment factor score in combination with fault data; converting the environmental factor score into an environmental risk weight, and weighting the environmental data to obtain a first risk influence value; correcting the first risk influence value according to the fault severity of the environmental factors at different baseline heights to obtain a second risk influence value, wherein the fault severity is obtained according to the fault frequency and fault loss of the fault data; constructing a position risk association degree and an equipment environment association degree according to the second risk influence value and the equipment health state data, and obtaining a cluster risk feature in combination with the equipment operation state data; and evaluating the cluster risk characteristics to obtain the distributed photovoltaic cluster operation risk. The method is used for solving the problems that existing photovoltaic cluster risk assessment is one-sided in angle and low in precision.

The invention relates to the technical field of high-altitude operation monitoring, and discloses a high-altitude operation anti-falling safety monitoring method based on data processing. The method comprises the steps of obtaining an environmental parameter set of a target operation area, and performing matching query in a historical accident database to determine a historical falling accident feature set; performing risk grade division on the falling accident feature set to obtain a plurality of graded historical falling accident feature sets, and traversing and extracting an environmental risk factor reference value and a risk influence range parameter; taking the risk influence range parameter as a boundary condition of a dynamic monitoring window, and performing multi-dimensional feature capture on the real-time motion data sequence of the high-altitude operation personnel by utilizing a plurality of configured dynamic monitoring windows to obtain a plurality of real-time motion feature sets; and carrying out space-time correlation fusion on the plurality of real-time motion feature sets to obtain a target space-time fusion motion feature set, and taking the target space-time fusion motion feature set as an anti-falling safety monitoring result. According to the method, environment and motion data are fused, and a precise monitoring basis is provided for high-altitude operation falling prevention.

This invention discloses a method for product board fault prediction and classification based on big data of electrical performance, specifically relating to the field of electronic engineering. It addresses the problem of existing technologies struggling to uniformly utilize multi-source electrical performance data and maintenance records for future fault prediction and classification. The method involves aggregating various information in a management system to obtain an electrical performance timeline, extracting electrical performance feature sequences from the timeline, and associating them with process information and maintenance results to form a lifecycle dataset. Based on process information and operational stages, fault prediction and classification models are trained in groups to construct a model library. Upon receiving new board electrical performance data, the method updates features, combines stage degradation comparison parameters and batch anomaly aggregation parameters, and uses a coefficient learning model to assess the risk impact, dynamically adjusting the risk level and fault type results output by the selected fault prediction model. Finally, it records actual faults and maintenance results, compares and adjusts the results, and updates the lifecycle dataset and model library to generate production operation and maintenance management decision-making information.

The invention discloses a cross-service network security detection processing method, which comprises the following steps of: acquiring data from a container during operation, forming a standardized container node behavior feature vector through context aggregation and vectorization, outputting a behavior deviation degree score through an anomaly detection model, identifying suspicious nodes by utilizing a dynamic threshold value, and finally, determining whether the suspicious nodes are abnormal or not. Creating container node situation identification information containing a propagation risk entropy value for the container node; then, on the basis of the service dependency graph, simulating the propagation process of the risk along the topology, and defining a cross-service potential risk influence domain; in the domain, performing gene coding and clustering on node behaviors, and extracting a common threat behavior mode; and finally, in combination with the tactical matching degree, the influence range and the business criticality, calculating the risk severity level, thereby completing the whole process from individual anomaly detection, associated risk propagation to threat mode grading.

This invention discloses an e-commerce order behavior risk assessment system based on IoT coding, comprising: a data acquisition module for collecting and preprocessing data to construct an order data sequence; a unified coding module for performing entity identification and unified coding processing to generate an object coding sequence; an association mapping module for performing association propagation analysis and relationship mapping processing to construct an order association graph; a risk propagation module for performing risk impact analysis and risk diffusion identification processing on the order association graph to generate a risk association sequence; an anomaly analysis module for performing abnormal behavior analysis on the risk association sequence to construct a risk feature sequence; and a risk assessment module for generating an order risk assessment result based on the risk feature sequence. This invention utilizes IoT unified coding and other technologies, possessing advantages such as strong association tracking capabilities, high accuracy in risk identification, high efficiency in anomaly detection, and high stability in risk assessment.